Research Papers
HoopEval: Individual Player Action Evaluation via Deep Reinforcement Learning
Authors:
Xing Wang, Yu Fu, Sheng Xu, Konstantinos Pelechrinis, Mingxin Zhang, Miguel Ángel Gómez Ruano, Guiliang Liu, Shaoliang Zhang
Abstract:
This paper presents HoopEval, a deep reinforcement learning framework for evaluating individual player actions in basketball using spatio-temporal tracking data. The approach models game dynamics and player interactions to estimate the value of both on-ball and off-ball decisions within their tactical context. By decomposing possession-level outcomes into fine-grained action evaluations, HoopEval provides interpretable measures of decision quality beyond traditional statistics. The results demonstrate its potential to support tactical analysis, player development, and data-driven coaching.